Low-Power Design of Fully Digital BPSK Modulator and Demodulator Utilizing Nanoscale FinFET for Smart Implants

This study aims to create a high-speed, low-power data transmission solution for implantable medical devices based on cutting-edge FinFET technology. The work examines the application of Binary Phase Shift Keying (BPSK) modulation through a transmission gate design, which provides an optimal blend of low resistance, high-speed performance, and minimal power consumption. Additionally, the work includes the design of a sine-to-square wave converter and a modulating signal generator. FinFET is employed owing to its high switching speed, low power consumption, low leakage current, and excellent tolerance of short channel effects. The design exhibits a steady electric field at the source end, a high electrostatic potential, and an improved ON current at low work function values using Sentaurus TCAD simulations of a 20nm FinFET, allowing high-speed data modulation in smart implants. A nonoverlapping phase generator, a low-power, current-starved gated ring oscillator, a frequency divider utilizing a True Single Phase Clock D-Flip-flop, and an XOR gate serving as a pulse counter are all featured in the design of the BPSK demodulator. This work is significant for its ability to drastically reduce power consumption to 1.75μW while retaining high data transmission speeds, making it perfect for next-generation implantable medical devices. With a 0.9 V power supply, this FinFET-based BPSK modulator and demodulator achieve a far lower power consumption than conventional CMOS-based designs, which increases device longevity and efficiency in settings with limited resources.

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Publication Details

Journal
Current Nanoscience
Published
2024-12-04
DOI
https://doi.org/10.2174/0115734137343365241122102200
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Low-Power Design of Fully Digital BPSK Modulator and Demodulator Utilizing Nanoscale FinFET for Smart Implants

Biswajit Jena, G. Lakshmi Priya, M. Keerthana, Kasturi Priyanka
Current Nanoscience
Advanced Memory and Neural Computing
article

Low-Power Design of Fully Digital BPSK Modulator and Demodulator Utilizing Nanoscale FinFET for Smart Implants

Biswajit Jena, G. Lakshmi Priya, M. Keerthana, Kasturi Priyanka
article en

Abstract

This study aims to create a high-speed, low-power data transmission solution for implantable medical devices based on cutting-edge FinFET technology. The work examines the application of Binary Phase Shift Keying (BPSK) modulation through a transmission gate design, which provides an optimal blend of low resistance, high-speed performance, and minimal power consumption. Additionally, the work includes the design of a sine-to-square wave converter and a modulating signal generator. FinFET is employed owing to its high switching speed, low power consumption, low leakage current, and excellent tolerance of short channel effects. The design exhibits a steady electric field at the source end, a high electrostatic potential, and an improved ON current at low work function values using Sentaurus TCAD simulations of a 20nm FinFET, allowing high-speed data modulation in smart implants. A nonoverlapping phase generator, a low-power, current-starved gated ring oscillator, a frequency divider utilizing a True Single Phase Clock D-Flip-flop, and an XOR gate serving as a pulse counter are all featured in the design of the BPSK demodulator. This work is significant for its ability to drastically reduce power consumption to 1.75μW while retaining high data transmission speeds, making it perfect for next-generation implantable medical devices. With a 0.9 V power supply, this FinFET-based BPSK modulator and demodulator achieve a far lower power consumption than conventional CMOS-based designs, which increases device longevity and efficiency in settings with limited resources.

Current NanoscienceVol. 21(6)
Vellore Institute of Technology University (IN)
Affordable and clean energy
Openalex Percentile: Top 80%
Advanced Memory and Neural Computing
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